Financial services organizations have large volumes of financial data that includes not only account balances or payment transactions, but also information such as customer FICO scores and credit history. Historically, organizations couldn’t do much with this data to improve their business. But new automated artificial intelligence (AI) methods make it possible to analyze data in real time. Financial organizations are increasingly using cloud-based, GPU-accelerated artificial intelligence (AI) and predictive analytics (ML) recommendation systems to analyze the vast amounts of financial data. The results of the analysis can be used to offer suggestions to customers to improve the customer experience, create new products, and provide financial organizations with new revenue streams.
What is a recommendation system?
Recommender systems, also called recommendation engines, are artificial intelligence systems used to suggest a product, service or information to a user. Recommender systems are based on user characteristics, preferences, history and data, so the recommendation is always personalized for a particular customer or user.
Use of financial recommendation systems to improve the customer experience
Financial services organizations are increasingly using recommendation systems to suggest new products, answer user questions, or analyze customer data to help customers solve problems. according to a 2021 Forbes Article, “Financial services firms can leverage ML/AI to understand their customers and lines of business more effectively. For example, many companies are using machine learning to power recommendation engines for financial products or customer engagement prompts for relationship management teams. It combines personal data, including how someone uses credit, their score and balances, and then suggests suitable products that fit people’s needs.”
Building an effective AI recommendation solution
Predictive analytics using AI recommendation systems require the analysis of massive amounts of data. Many financial organizations have legacy infrastructure, limited budgets for AI development, and staff that lack the data science skills needed to implement AI recommendation algorithms. This research from the Forrester report shows that “Approximately two-thirds (64%) of technical decision makers are not fully confident in their ability to meet their organization’s AI goals given current resources.” Training ML recommendation models requires huge computational resources. Legacy infrastructure with CPU-based processing cannot handle the processing speeds required, switching to a GPU-based infrastructure provides much faster processing and training for ML inference models.
According to the forrester survey, “What organizations need are pre-built, configurable AI cloud services. Cloud AI services allow developers to access a wealth of AI capabilities through APIs to drive application innovation without the need for data science expertise.” Moving to a cloud-based AI solution that includes pre-built AI models results in faster implementation time and gives organizations access to AI models that have been responsibly built and tested.
Using cloud-based, GPU-accelerated AI and ML solutions removes the barriers financial services institutions face in developing AI and ML recommendation algorithms. The “Survey on the state of AI in financial services” found that “companies are experiencing significant financial benefit from enabling AI across the enterprise. More than 30 percent of respondents said AI is increasing annual revenue by more than 10 percent, while more than a quarter said AI is reducing annual costs by more than 10 percent.
Referral System Example: Helping a Client Improve Liquidity
A bank recommendation system was used to analyze real-time payment data for a customer’s business. The analysis reveals that a small merchant client regularly has negative liquidity on the third day of every month, so he would not be able to deal with any urgent problem or opportunity that arises at that time due to the cash flow problem. Based on the analysis, the bank could offer the client a liquidity analysis service to help improve cash flow and better anticipate and manage day-to-day operations.
Technology partners provide cloud-based, GPU-accelerated AI recommendation solutions
Microsoft and NVIDIA have a long history of working together to support financial institutions in providing technology to support AI and ML solutions, such as recommender systems. Wearing microsoft blue cloud, and the NVIDIA AI The platform provides scalable and accelerated resources needed to run AI/ML algorithms, routines, and libraries.
The partnership between Microsoft and NVIDIA makes NVIDIA’s powerful GPU acceleration available to financial institutions. the Azure Machine Learning Service integrates NVIDIA open source RAPID software library that enables machine learning users to accelerate their processes with NVIDIA GPUs. NVIDIA TensorRT acceleration library added to ONNX runtime to speed up deep learning inference. Azure supports NVIDIA Tensor Core T4 Graphics Processing Units (GPUs)and the NVIDIA DGX H100 system that are optimized for cost-effective deployment of machine learning inference or analytical workloads.
Microsoft cloud-based solutions for financial recommendation systems
move to the Microsoft Azure cloud solution provides financial institutions with a complete set of compute, network, and storage resources integrated with workload services capable of handling the requirements of recommendation algorithm processing. Microsoft Azure enables developers to build and train new AI models faster with automated machine learning, autoscaling cloud computing, and built-in DevOps.
Finding or developing the right financial recommendation system can be a time-consuming process for data scientists. Microsoft provides a GitHub repository with Python best practice examples to make it easy to construction and evaluation of recommender systems wearing Azure Machine Learning Services.
Summary
Historically, financial services organizations did not have an automated way to analyze their massive amounts of data. Predictive analytics backed by GPU-based cloud solutions using AI and ML recommendation systems can analyze large amounts of fast-moving data in real time. This analytics can generate valuable insights into customer buying behavior, allowing financial organizations to customize offers for individual customers. The use of recommendation systems can also provide information that financial institutions can use to help build new business models or revenue streams.